Graduate Studies


First Advisor

Michael P Sealy

Date of this Version



A THESIS Presented to the Faculty of the Graduate College at the University of Nebraska In Partial Fulfillment of Requirements for the Degree of Master of Science, Major: Mechanical Engineering and Applied Mechanics, Under the Supervision of Professor Michael P. Sealy. Lincoln, Nebraska: December 2022

Copyright © 2022 Anthony W. Wilson


Metal printed by additive manufacturing will respond differently to the intense heat of fusion and rapid cooling rates necessary to build layer by layer. Altering the parameters of laser power, scanning velocity, layer height, and others achieves the desired microstructure and material response. Printed parts can have localized elasticity, ductility, and toughness responses different from global behavior. Localized properties work as an indicator of failure before failure happens. When a metal fails, cracks propagate at predictable fracture toughness values. This research considers two methods of analysis, the characterization of the fracture behavior of 316L stainless steel and the relationship between residual stresses and milling power in 420 stainless steel to predict the localized stress. The standard print parameters for fracture analysis varied by ±20% for laser power and scanning velocity. The changes in energy density provided a window to examine process variations. Mechanical properties were determined by ultrasonic surface wave analysis, notched by wire EDM and fracture tested using ASTM E1820. In terms of fracture behavior, the key results demonstrated the influence of process parameters on fracture toughness and stability. Low energy densities exhibited low fracture toughness (< 110 kJ/m2) and unstable fracture. High energy densities exhibited higher fracture toughness (225-425 kJ/m2) and stable crack growth. In terms of energy analysis, the key result demonstrated that machining-based energy consumption was able to reasonably predict the 2D residual stress state. Using data dependent systems, the correlation between frequency and minimum stress was 77% and between frequency and average stress was 64%.

Advisor: Michael P. Sealy